Predicting stock splits using ensemble machine learning and SMOTE oversampling

Ang Li; Mark Liu; Simon Sheather

doi:10.1016/j.pacfin.2023.101948

Predicting stock splits using ensemble machine learning and SMOTE oversampling

Ang Li, Mark Liu, Simon Sheather

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

This study predicts stock splits using two ensemble machine learning techniques: gradient boosting machines (GBMs) and random forests (RFs). The goal is to form implementable portfolios based on positive predictions to generate abnormal returns. Since splits are rare events, we use SMOTE oversampling to synthesize new observations of splits in the sample to improve predictions. When predicting stock splits in the next quarter, GBM and RF achieve area under the receiver operating characteristic curve (AUC) scores of around 0.86 and 0.87, respectively. GBM and RF predictions generate monthly five-factor alphas (Fama and French, 2015) of 0.26% and 0.95% among stocks in the smallest size quintile. Three important features for predicting stock splits in both ensemble ML methods are current price levels, the ratio of current price to the price at last split, and stock returns in the past twelve months. When predicting stock splits in the next year, GBMs generate monthly five-factor alphas of 0.38% among small stocks.

Original language	English
Article number	101948
Journal	Pacific Basin Finance Journal
Volume	78
DOIs	https://doi.org/10.1016/j.pacfin.2023.101948
State	Published - Apr 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.

Funding

☆ We thank Leonce Bargeron, Alex Brefeld, Charlie Clarke, Grant Clayton, Koustav De, Will Gerken, Russell Jame, Paulo Manoel, Peter Qi, Fabrice Riva (discussant), Jake Smith (discussant), Spencer Stone, Mao Ye, seminar participants at the University of Kentucky, and session participants at the 2022 Commonwealth Computational Summit, 12th Financial Markets and Corporate Governance Conference, and the 2022 FMA Annual Meeting for helpful comments. All errors and omissions are our own.

Funders	Funder number
University of Kentucky

Keywords

Ensemble machine learning
Gradient boosting machines
Hyperparameter tuning
Random forests
SHAP feature importance
SMOTE oversampling
Stock splits

ASJC Scopus subject areas

Finance
Economics and Econometrics

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10.1016/j.pacfin.2023.101948

Cite this

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title = "Predicting stock splits using ensemble machine learning and SMOTE oversampling",

abstract = "This study predicts stock splits using two ensemble machine learning techniques: gradient boosting machines (GBMs) and random forests (RFs). The goal is to form implementable portfolios based on positive predictions to generate abnormal returns. Since splits are rare events, we use SMOTE oversampling to synthesize new observations of splits in the sample to improve predictions. When predicting stock splits in the next quarter, GBM and RF achieve area under the receiver operating characteristic curve (AUC) scores of around 0.86 and 0.87, respectively. GBM and RF predictions generate monthly five-factor alphas (Fama and French, 2015) of 0.26% and 0.95% among stocks in the smallest size quintile. Three important features for predicting stock splits in both ensemble ML methods are current price levels, the ratio of current price to the price at last split, and stock returns in the past twelve months. When predicting stock splits in the next year, GBMs generate monthly five-factor alphas of 0.38% among small stocks.",

keywords = "Ensemble machine learning, Gradient boosting machines, Hyperparameter tuning, Random forests, SHAP feature importance, SMOTE oversampling, Stock splits",

author = "Ang Li and Mark Liu and Simon Sheather",

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AU - Liu, Mark

AU - Sheather, Simon

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AB - This study predicts stock splits using two ensemble machine learning techniques: gradient boosting machines (GBMs) and random forests (RFs). The goal is to form implementable portfolios based on positive predictions to generate abnormal returns. Since splits are rare events, we use SMOTE oversampling to synthesize new observations of splits in the sample to improve predictions. When predicting stock splits in the next quarter, GBM and RF achieve area under the receiver operating characteristic curve (AUC) scores of around 0.86 and 0.87, respectively. GBM and RF predictions generate monthly five-factor alphas (Fama and French, 2015) of 0.26% and 0.95% among stocks in the smallest size quintile. Three important features for predicting stock splits in both ensemble ML methods are current price levels, the ratio of current price to the price at last split, and stock returns in the past twelve months. When predicting stock splits in the next year, GBMs generate monthly five-factor alphas of 0.38% among small stocks.

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KW - Random forests

KW - SHAP feature importance

KW - SMOTE oversampling

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Predicting stock splits using ensemble machine learning and SMOTE oversampling

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

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